A Formant Frequency Estimation Scheme for Speech Signals in the Presence of Noise

In this paper, a new scheme for the estimation of formant frequencies of noise-corrupted speech signals is presented. A once-repeated autocorrelation function (ORACF) of the observed noisy speech signal is proposed to employ in a linear predictive based formant estimation method. It has been shown that the ORACF is capable of reducing the effect of additive noise significantly and if, instead of conventional ACF, ORACF is used in a modified form of least-squares Yule-Walker equations, a better performance in the formant estimation is achieved. Moreover, a frequency-domain algorithm is incorporated in the proposed scheme to avoid the possible estimation error in extracting a formant with low energy. The proposed algorithm has been tested on synthetic and natural vowels as well as some naturally spoken sentences in the presence of additive noise. The experimental results demonstrate a better performance obtained by the proposed scheme in comparison to some of the existing methods at low levels of signal-to-noise ratio (SNR).